低分辨率遥感影像中道路的全自动提取方法研究

Research on Roads Automatic Extraction from Low Resolution Remote Sensing Image

从Marr视觉计算理论出发,利用低分辨率遥感影像的特点,提出了一种基于边缘线段感知编组和动态规划跟踪线段的道路信息全自动提取方法。首先分析了低分辨率影像中道路的辐射特性和几何特性,以得到道路模型。然后在底层处理阶段对影像进行边缘检测、无效线段去除等预处理;中层处理阶段采用基于上下文关联的感知线段编组法得到候选道路段,并由道路段的置信度阈值确定道路种子点;最后在高层处理阶段提出基于动态规划的道路跟踪算法得到候选道路,并且采用知识推理去除部分虚警。实验结果表明:(1)对图像中背景干扰较大的山区道路和复杂的城区道路网均有较好的识别效果;(2)识别过程全自动进行,没有人工干预,因此计算效率相比其他方法有一定的优势;(3)由不同传感器卫星获取的影像如L7,SPOT,SAR等,算法均能进行有效识别,具有很好的普适性和稳健性。

Image understanding is generally defined as the construction of explicit,meaningful descriptions of the structure and the properties of the 3-dimensional world from 2-dimensional images.A conceptual framework for image understanding is based on Marr's concept of visual perception as computational process.Marr postulated a hierarchical architecture for vision systems with different intermediate representations and processing levels(low,middle and higher level vision).According to the description of Marr's machine vision theory and the characteristics of low resolution remote sensing images,this paper proposes an automatic main-road extraction method,which is based on line segment perceptual grouping and dynamic programming.The method exploits the road model in low resolution remote sensing images at low level and locates the road seeds automatically by line segment perceptual grouping at middle level and then tracks the road seeds to extract the road networks by dynamic programming at high level.Firstly we illustrate the road model in low resolution remote sensing images based on analyzing road characteristics,such as photometric,geometric,topological and contextual characteristics and so on.To increase the precision of road object recognition and to reduce the effects of noise,source images are preprocessed ahead,which include contrast stretching,edge information detection with canny operator and redundant line segments elimination.Edge detection is crucial to line segment perceptual grouping,thus canny operator is applied because of its characteristics of high position precision,single pixel width and low error rate.In the process of line segments elimination,the length and curvature of line segments are considered as the decisive factors to the elimination of redundant line segments.But the directions of the beginning and end line segments are recorded to assist the decision.At middle level,edge line segments are grouped by perceptual grouping technology based on contextual line segments to generate latent road edge line segments.After that,several road seeds are located by computing the latent road edge line segment groups.The relationship of gray value between the regions shaped by the latent road edge line segment and their background is exploited to locate the road seeds thereinto.Then a new road tracking approach using dynamic programming is adopted at high level.The approach introduces the concept of minimized cost route and extends the primitive segment which is formed by direct connection of road seeds to the whole road network in light of minimized cost route.Finally false alarms are eliminated by knowledge inference method,in which inference rules are attained based on the geographic characteristics of road networks in low resolution remote sensing images.We conducted experiments on three datasets of low resolution remote sensing images,which include the Landsat7(B80 band) image with 15m-resolution,the SPOT image of San Diego district with 10m-resolution and the SAR(Synthetic Aperture Radar) image with 12.5m-resolution.Correctness and completeness are introduced to make objective evaluation on the effectiveness of the method.In this way,reference data,which means the road network plotted by observer,should be defined ahead.Experimental results show that our proposed method has high correctness,especially in Landsat7 remote sensing image(98.7%).Meanwhile the completeness criteria gained from all the source datasets is comparatively high.The lowest value(88.1%) appears in SAR image probably due to the speckle noises.Moreover the followings are proved by the experimental results:(1) the proposed method is effective in lowresolution remote sensing images(high resolution remote sensing images can be sampled to generate its low resolution counterpart).Especially the images contain some sparse rural roads and intricate city road network;(2) the method is completely automatic and shows better computation efficiency than others,especially compared to semi-automatic road detection methods which need human and computer interaction;(3) the method shows robustness and good performance in remote sensing images such as Landsat7,SPOT and SAR.